Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations5700
Missing cells0
Missing cells (%)0.0%
Duplicate rows479
Duplicate rows (%)8.4%
Total size in memory1.4 MiB
Average record size in memory260.0 B

Variable types

Numeric15
Categorical4
Text2

Alerts

Dataset has 479 (8.4%) duplicate rowsDuplicates
Day Charge is highly overall correlated with Day MinsHigh correlation
Day Mins is highly overall correlated with Day ChargeHigh correlation
Eve Charge is highly overall correlated with Eve MinsHigh correlation
Eve Mins is highly overall correlated with Eve ChargeHigh correlation
Intl Charge is highly overall correlated with Intl MinsHigh correlation
Intl Mins is highly overall correlated with Intl ChargeHigh correlation
Night Charge is highly overall correlated with Night MinsHigh correlation
Night Mins is highly overall correlated with Night ChargeHigh correlation
VMail Message is highly overall correlated with VMail PlanHigh correlation
VMail Plan is highly overall correlated with VMail MessageHigh correlation
Churn is uniformly distributedUniform
VMail Message has 4382 (76.9%) zerosZeros
CustServ Calls has 1146 (20.1%) zerosZeros

Reproduction

Analysis started2025-10-31 14:56:05.867641
Analysis finished2025-10-31 14:56:39.833966
Duration33.97 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Account Length
Real number (ℝ)

Distinct212
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.42158
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:39.999740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q175
median101
Q3127
95-th percentile169
Maximum243
Range242
Interquartile range (IQR)52

Descriptive statistics

Standard deviation39.52821
Coefficient of variation (CV)0.38974161
Kurtosis-0.054424627
Mean101.42158
Median Absolute Deviation (MAD)26
Skewness0.090677977
Sum578103
Variance1562.4794
MonotonicityNot monotonic
2025-10-31T14:56:40.234372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9383
 
1.5%
10876
 
1.3%
8876
 
1.3%
10076
 
1.3%
11974
 
1.3%
10574
 
1.3%
12769
 
1.2%
10168
 
1.2%
11367
 
1.2%
7667
 
1.2%
Other values (202)4970
87.2%
ValueCountFrequency (%)
114
0.2%
27
0.1%
35
 
0.1%
41
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
72
 
< 0.1%
81
 
< 0.1%
93
 
0.1%
103
 
0.1%
ValueCountFrequency (%)
2431
 
< 0.1%
2321
 
< 0.1%
2257
0.1%
2244
0.1%
2211
 
< 0.1%
2172
 
< 0.1%
2151
 
< 0.1%
2124
0.1%
2102
 
< 0.1%
2099
0.2%

VMail Message
Real number (ℝ)

High correlation  Zeros 

Distinct46
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8421053
Minimum0
Maximum51
Zeros4382
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:40.470812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile36
Maximum51
Range51
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.979636
Coefficient of variation (CV)1.8970237
Kurtosis0.69957509
Mean6.8421053
Median Absolute Deviation (MAD)0
Skewness1.5300356
Sum39000
Variance168.47094
MonotonicityNot monotonic
2025-10-31T14:56:40.708899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
04382
76.9%
2997
 
1.7%
3185
 
1.5%
2879
 
1.4%
2677
 
1.4%
3377
 
1.4%
3262
 
1.1%
3662
 
1.1%
2760
 
1.1%
3051
 
0.9%
Other values (36)668
 
11.7%
ValueCountFrequency (%)
04382
76.9%
41
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
101
 
< 0.1%
112
 
< 0.1%
126
 
0.1%
134
 
0.1%
147
 
0.1%
1517
 
0.3%
ValueCountFrequency (%)
511
 
< 0.1%
502
 
< 0.1%
491
 
< 0.1%
485
 
0.1%
473
 
0.1%
464
 
0.1%
4512
0.2%
4414
0.2%
439
 
0.2%
4229
0.5%

Day Mins
Real number (ℝ)

High correlation 

Distinct1667
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.04302
Minimum0
Maximum350.8
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:40.890522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.7
Q1145.6
median189.1
Q3239
95-th percentile291.8
Maximum350.8
Range350.8
Interquartile range (IQR)93.4

Descriptive statistics

Standard deviation62.753767
Coefficient of variation (CV)0.32847977
Kurtosis-0.52736258
Mean191.04302
Median Absolute Deviation (MAD)46.7
Skewness0.006447424
Sum1088945.2
Variance3938.0353
MonotonicityNot monotonic
2025-10-31T14:56:41.049450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242.221
 
0.4%
27819
 
0.3%
143.218
 
0.3%
162.318
 
0.3%
15418
 
0.3%
167.817
 
0.3%
239.717
 
0.3%
259.416
 
0.3%
256.416
 
0.3%
255.116
 
0.3%
Other values (1657)5524
96.9%
ValueCountFrequency (%)
05
0.1%
2.61
 
< 0.1%
7.81
 
< 0.1%
7.91
 
< 0.1%
12.51
 
< 0.1%
17.61
 
< 0.1%
18.91
 
< 0.1%
19.51
 
< 0.1%
25.91
 
< 0.1%
271
 
< 0.1%
ValueCountFrequency (%)
350.83
0.1%
346.85
0.1%
345.37
0.1%
337.44
0.1%
335.56
0.1%
334.34
0.1%
332.95
0.1%
329.85
0.1%
328.17
0.1%
326.53
0.1%

Eve Mins
Real number (ℝ)

High correlation 

Distinct1611
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.45982
Minimum0
Maximum363.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:41.198614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile120.5
Q1169.775
median206.35
Q3240.925
95-th percentile288.4
Maximum363.7
Range363.7
Interquartile range (IQR)71.15

Descriptive statistics

Standard deviation51.535524
Coefficient of variation (CV)0.25083018
Kurtosis0.013039555
Mean205.45982
Median Absolute Deviation (MAD)36.2
Skewness0.026267533
Sum1171121
Variance2655.9103
MonotonicityNot monotonic
2025-10-31T14:56:41.348355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211.324
 
0.4%
226.124
 
0.4%
205.720
 
0.4%
253.420
 
0.4%
176.720
 
0.4%
169.920
 
0.4%
167.919
 
0.3%
23018
 
0.3%
267.818
 
0.3%
169.818
 
0.3%
Other values (1601)5499
96.5%
ValueCountFrequency (%)
01
< 0.1%
31.21
< 0.1%
42.21
< 0.1%
42.51
< 0.1%
43.91
< 0.1%
48.11
< 0.1%
49.21
< 0.1%
52.91
< 0.1%
561
< 0.1%
58.61
< 0.1%
ValueCountFrequency (%)
363.78
0.1%
361.81
 
< 0.1%
354.21
 
< 0.1%
351.61
 
< 0.1%
350.97
0.1%
350.53
 
0.1%
348.51
 
< 0.1%
347.37
0.1%
341.31
 
< 0.1%
339.99
0.2%

Night Mins
Real number (ℝ)

High correlation 

Distinct1591
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.87554
Minimum23.2
Maximum395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:41.495776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile122.2
Q1169
median203.7
Q3238.4
95-th percentile283.6
Maximum395
Range371.8
Interquartile range (IQR)69.4

Descriptive statistics

Standard deviation49.510957
Coefficient of variation (CV)0.24404596
Kurtosis0.055614001
Mean202.87554
Median Absolute Deviation (MAD)34.7
Skewness0.001119508
Sum1156390.6
Variance2451.3349
MonotonicityNot monotonic
2025-10-31T14:56:41.657053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214.226
 
0.5%
214.521
 
0.4%
255.321
 
0.4%
178.121
 
0.4%
191.420
 
0.4%
194.119
 
0.3%
203.719
 
0.3%
224.718
 
0.3%
212.317
 
0.3%
24517
 
0.3%
Other values (1581)5501
96.5%
ValueCountFrequency (%)
23.21
 
< 0.1%
43.71
 
< 0.1%
451
 
< 0.1%
47.45
0.1%
50.12
 
< 0.1%
53.31
 
< 0.1%
541
 
< 0.1%
54.51
 
< 0.1%
56.61
 
< 0.1%
57.51
 
< 0.1%
ValueCountFrequency (%)
3951
 
< 0.1%
381.91
 
< 0.1%
377.51
 
< 0.1%
367.71
 
< 0.1%
364.91
 
< 0.1%
364.31
 
< 0.1%
354.910
0.2%
352.51
 
< 0.1%
352.21
 
< 0.1%
350.21
 
< 0.1%

Intl Mins
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.406
Minimum0
Maximum20
Zeros18
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:41.798216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q18.6
median10.4
Q312.3
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation2.7991537
Coefficient of variation (CV)0.2689942
Kurtosis0.33232389
Mean10.406
Median Absolute Deviation (MAD)1.8
Skewness-0.12606241
Sum59314.2
Variance7.8352614
MonotonicityNot monotonic
2025-10-31T14:56:41.947857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.9109
 
1.9%
11101
 
1.8%
11.199
 
1.7%
11.597
 
1.7%
1097
 
1.7%
8.894
 
1.6%
10.392
 
1.6%
11.390
 
1.6%
10.189
 
1.6%
8.988
 
1.5%
Other values (152)4744
83.2%
ValueCountFrequency (%)
018
0.3%
1.11
 
< 0.1%
1.31
 
< 0.1%
26
 
0.1%
2.12
 
< 0.1%
2.21
 
< 0.1%
2.41
 
< 0.1%
2.51
 
< 0.1%
2.61
 
< 0.1%
2.71
 
< 0.1%
ValueCountFrequency (%)
202
 
< 0.1%
18.91
 
< 0.1%
18.41
 
< 0.1%
18.36
0.1%
18.22
 
< 0.1%
183
 
0.1%
17.910
0.2%
17.82
 
< 0.1%
17.67
0.1%
17.58
0.1%

CustServ Calls
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8540351
Minimum0
Maximum9
Zeros1146
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:42.056350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6104457
Coefficient of variation (CV)0.8686166
Kurtosis1.0205833
Mean1.8540351
Median Absolute Deviation (MAD)1
Skewness1.0348445
Sum10568
Variance2.5935352
MonotonicityNot monotonic
2025-10-31T14:56:42.150776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11772
31.1%
21161
20.4%
01146
20.1%
3667
 
11.7%
4538
 
9.4%
5270
 
4.7%
690
 
1.6%
731
 
0.5%
914
 
0.2%
811
 
0.2%
ValueCountFrequency (%)
01146
20.1%
11772
31.1%
21161
20.4%
3667
 
11.7%
4538
 
9.4%
5270
 
4.7%
690
 
1.6%
731
 
0.5%
811
 
0.2%
914
 
0.2%
ValueCountFrequency (%)
914
 
0.2%
811
 
0.2%
731
 
0.5%
690
 
1.6%
5270
 
4.7%
4538
 
9.4%
3667
 
11.7%
21161
20.4%
11772
31.1%
01146
20.1%

Intl Plan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size278.4 KiB
0
4696 
1
1004 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
04696
82.4%
11004
 
17.6%

Length

2025-10-31T14:56:42.259532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T14:56:42.342850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
04696
82.4%
11004
 
17.6%

Most occurring characters

ValueCountFrequency (%)
04696
82.4%
11004
 
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)5700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04696
82.4%
11004
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04696
82.4%
11004
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04696
82.4%
11004
 
17.6%

VMail Plan
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size278.4 KiB
0
4382 
1
1318 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04382
76.9%
11318
 
23.1%

Length

2025-10-31T14:56:42.428983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T14:56:42.505866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
04382
76.9%
11318
 
23.1%

Most occurring characters

ValueCountFrequency (%)
04382
76.9%
11318
 
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04382
76.9%
11318
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04382
76.9%
11318
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04382
76.9%
11318
 
23.1%

Day Calls
Real number (ℝ)

Distinct119
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.58912
Minimum0
Maximum165
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:42.627157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q187
median101
Q3114
95-th percentile134
Maximum165
Range165
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.56904
Coefficient of variation (CV)0.20448573
Kurtosis0.40851804
Mean100.58912
Median Absolute Deviation (MAD)14
Skewness-0.19191152
Sum573358
Variance423.08541
MonotonicityNot monotonic
2025-10-31T14:56:42.775841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106138
 
2.4%
105137
 
2.4%
108136
 
2.4%
112120
 
2.1%
95118
 
2.1%
104116
 
2.0%
101115
 
2.0%
99114
 
2.0%
91114
 
2.0%
103112
 
2.0%
Other values (109)4480
78.6%
ValueCountFrequency (%)
05
 
0.1%
301
 
< 0.1%
351
 
< 0.1%
361
 
< 0.1%
402
 
< 0.1%
428
0.1%
4418
0.3%
458
0.1%
478
0.1%
4811
0.2%
ValueCountFrequency (%)
1657
0.1%
1631
 
< 0.1%
1601
 
< 0.1%
1583
 
0.1%
1571
 
< 0.1%
1566
0.1%
1521
 
< 0.1%
15114
0.2%
1506
0.1%
1491
 
< 0.1%

Day Charge
Real number (ℝ)

High correlation 

Distinct1667
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.477846
Minimum0
Maximum59.64
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:42.927263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.25
Q124.75
median32.15
Q340.63
95-th percentile49.61
Maximum59.64
Range59.64
Interquartile range (IQR)15.88

Descriptive statistics

Standard deviation10.66818
Coefficient of variation (CV)0.3284756
Kurtosis-0.52728316
Mean32.477846
Median Absolute Deviation (MAD)7.94
Skewness0.0064476652
Sum185123.72
Variance113.81006
MonotonicityNot monotonic
2025-10-31T14:56:43.080344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.1721
 
0.4%
47.2619
 
0.3%
24.3418
 
0.3%
27.5918
 
0.3%
26.1818
 
0.3%
28.5317
 
0.3%
40.7517
 
0.3%
44.116
 
0.3%
43.5916
 
0.3%
43.3716
 
0.3%
Other values (1657)5524
96.9%
ValueCountFrequency (%)
05
0.1%
0.441
 
< 0.1%
1.331
 
< 0.1%
1.341
 
< 0.1%
2.131
 
< 0.1%
2.991
 
< 0.1%
3.211
 
< 0.1%
3.321
 
< 0.1%
4.41
 
< 0.1%
4.591
 
< 0.1%
ValueCountFrequency (%)
59.643
0.1%
58.965
0.1%
58.77
0.1%
57.364
0.1%
57.046
0.1%
56.834
0.1%
56.595
0.1%
56.075
0.1%
55.787
0.1%
55.513
0.1%

Eve Calls
Real number (ℝ)

Distinct123
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.22895
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:43.228394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.696273
Coefficient of variation (CV)0.19651282
Kurtosis0.18450815
Mean100.22895
Median Absolute Deviation (MAD)13
Skewness0.0046677901
Sum571305
Variance387.94319
MonotonicityNot monotonic
2025-10-31T14:56:43.388258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94156
 
2.7%
102137
 
2.4%
96133
 
2.3%
110128
 
2.2%
108127
 
2.2%
97126
 
2.2%
88125
 
2.2%
105123
 
2.2%
111120
 
2.1%
92118
 
2.1%
Other values (113)4407
77.3%
ValueCountFrequency (%)
01
 
< 0.1%
121
 
< 0.1%
361
 
< 0.1%
371
 
< 0.1%
421
 
< 0.1%
431
 
< 0.1%
441
 
< 0.1%
451
 
< 0.1%
463
 
0.1%
4818
0.3%
ValueCountFrequency (%)
1701
 
< 0.1%
1688
0.1%
1641
 
< 0.1%
1599
0.2%
1571
 
< 0.1%
1561
 
< 0.1%
1553
 
0.1%
1542
 
< 0.1%
1531
 
< 0.1%
1526
0.1%

Eve Charge
Real number (ℝ)

High correlation 

Distinct1440
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.464219
Minimum0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:43.537895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.24
Q114.4275
median17.54
Q320.4825
95-th percentile24.51
Maximum30.91
Range30.91
Interquartile range (IQR)6.055

Descriptive statistics

Standard deviation4.3804281
Coefficient of variation (CV)0.25082301
Kurtosis0.012968658
Mean17.464219
Median Absolute Deviation (MAD)3.075
Skewness0.026256104
Sum99546.05
Variance19.18815
MonotonicityNot monotonic
2025-10-31T14:56:43.703311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.7126
 
0.5%
17.9624
 
0.4%
19.2224
 
0.4%
17.6524
 
0.4%
17.4823
 
0.4%
15.0220
 
0.4%
21.5420
 
0.4%
20.0920
 
0.4%
14.4420
 
0.4%
14.3719
 
0.3%
Other values (1430)5480
96.1%
ValueCountFrequency (%)
01
< 0.1%
2.651
< 0.1%
3.591
< 0.1%
3.611
< 0.1%
3.731
< 0.1%
4.091
< 0.1%
4.181
< 0.1%
4.51
< 0.1%
4.761
< 0.1%
4.981
< 0.1%
ValueCountFrequency (%)
30.918
0.1%
30.751
 
< 0.1%
30.111
 
< 0.1%
29.891
 
< 0.1%
29.837
0.1%
29.793
 
0.1%
29.621
 
< 0.1%
29.527
0.1%
29.011
 
< 0.1%
28.899
0.2%

Night Calls
Real number (ℝ)

Distinct120
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.05386
Minimum33
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:43.853317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile68
Q186
median100
Q3114
95-th percentile132
Maximum175
Range142
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.671484
Coefficient of variation (CV)0.19660894
Kurtosis-0.20384578
Mean100.05386
Median Absolute Deviation (MAD)14
Skewness0.056681806
Sum570307
Variance386.96727
MonotonicityNot monotonic
2025-10-31T14:56:44.002908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106141
 
2.5%
104129
 
2.3%
100125
 
2.2%
98124
 
2.2%
105123
 
2.2%
102121
 
2.1%
97115
 
2.0%
91114
 
2.0%
95111
 
1.9%
94108
 
1.9%
Other values (110)4489
78.8%
ValueCountFrequency (%)
331
 
< 0.1%
361
 
< 0.1%
381
 
< 0.1%
422
 
< 0.1%
441
 
< 0.1%
461
 
< 0.1%
481
 
< 0.1%
499
0.2%
502
 
< 0.1%
518
0.1%
ValueCountFrequency (%)
1751
 
< 0.1%
1661
 
< 0.1%
1641
 
< 0.1%
1587
0.1%
1572
 
< 0.1%
1562
 
< 0.1%
1552
 
< 0.1%
1542
 
< 0.1%
1533
 
0.1%
15211
0.2%

Night Charge
Real number (ℝ)

High correlation 

Distinct933
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1294596
Minimum1.04
Maximum17.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:44.148092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile5.5
Q17.61
median9.17
Q310.73
95-th percentile12.76
Maximum17.77
Range16.73
Interquartile range (IQR)3.12

Descriptive statistics

Standard deviation2.2280496
Coefficient of variation (CV)0.24405054
Kurtosis0.055572006
Mean9.1294596
Median Absolute Deviation (MAD)1.56
Skewness0.00098296522
Sum52037.92
Variance4.964205
MonotonicityNot monotonic
2025-10-31T14:56:44.289325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0137
 
0.6%
9.6435
 
0.6%
9.433
 
0.6%
7.7930
 
0.5%
8.8829
 
0.5%
9.4527
 
0.5%
9.1826
 
0.5%
6.1225
 
0.4%
10.2225
 
0.4%
9.6525
 
0.4%
Other values (923)5408
94.9%
ValueCountFrequency (%)
1.041
 
< 0.1%
1.971
 
< 0.1%
2.031
 
< 0.1%
2.135
0.1%
2.252
 
< 0.1%
2.41
 
< 0.1%
2.431
 
< 0.1%
2.451
 
< 0.1%
2.551
 
< 0.1%
2.591
 
< 0.1%
ValueCountFrequency (%)
17.771
 
< 0.1%
17.191
 
< 0.1%
16.991
 
< 0.1%
16.551
 
< 0.1%
16.421
 
< 0.1%
16.391
 
< 0.1%
15.9710
0.2%
15.861
 
< 0.1%
15.851
 
< 0.1%
15.761
 
< 0.1%

Intl Calls
Real number (ℝ)

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3531579
Minimum0
Maximum20
Zeros18
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:44.421747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.517672
Coefficient of variation (CV)0.57835531
Kurtosis3.8108446
Mean4.3531579
Median Absolute Deviation (MAD)1
Skewness1.4912706
Sum24813
Variance6.3386723
MonotonicityNot monotonic
2025-10-31T14:56:44.545444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
31195
21.0%
2995
17.5%
4985
17.3%
5717
12.6%
6535
9.4%
7357
 
6.3%
1317
 
5.6%
8185
 
3.2%
9177
 
3.1%
1083
 
1.5%
Other values (11)154
 
2.7%
ValueCountFrequency (%)
018
 
0.3%
1317
 
5.6%
2995
17.5%
31195
21.0%
4985
17.3%
5717
12.6%
6535
9.4%
7357
 
6.3%
8185
 
3.2%
9177
 
3.1%
ValueCountFrequency (%)
206
 
0.1%
191
 
< 0.1%
183
 
0.1%
171
 
< 0.1%
162
 
< 0.1%
1526
0.5%
1410
 
0.2%
1318
 
0.3%
1219
 
0.3%
1150
0.9%

Intl Charge
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8101351
Minimum0
Maximum5.4
Zeros18
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2025-10-31T14:56:44.704524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.57
Q12.32
median2.81
Q33.32
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75577194
Coefficient of variation (CV)0.26894506
Kurtosis0.33139518
Mean2.8101351
Median Absolute Deviation (MAD)0.49
Skewness-0.12639617
Sum16017.77
Variance0.57119123
MonotonicityNot monotonic
2025-10-31T14:56:44.847927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.94109
 
1.9%
2.97101
 
1.8%
399
 
1.7%
3.1197
 
1.7%
2.797
 
1.7%
2.3894
 
1.6%
2.7892
 
1.6%
3.0590
 
1.6%
2.7389
 
1.6%
2.488
 
1.5%
Other values (152)4744
83.2%
ValueCountFrequency (%)
018
0.3%
0.31
 
< 0.1%
0.351
 
< 0.1%
0.546
 
0.1%
0.572
 
< 0.1%
0.591
 
< 0.1%
0.651
 
< 0.1%
0.681
 
< 0.1%
0.71
 
< 0.1%
0.731
 
< 0.1%
ValueCountFrequency (%)
5.42
 
< 0.1%
5.11
 
< 0.1%
4.971
 
< 0.1%
4.946
0.1%
4.912
 
< 0.1%
4.863
 
0.1%
4.8310
0.2%
4.812
 
< 0.1%
4.757
0.1%
4.738
0.1%

State
Text

Distinct51
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size284.0 KiB
2025-10-31T14:56:45.093765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters11400
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowNJ
4th rowOH
5th rowOK
ValueCountFrequency (%)
ny168
 
2.9%
wv159
 
2.8%
tx158
 
2.8%
nj155
 
2.7%
mn155
 
2.7%
mi151
 
2.6%
md149
 
2.6%
ks143
 
2.5%
or142
 
2.5%
nv137
 
2.4%
Other values (41)4183
73.4%
2025-10-31T14:56:45.436215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N1290
 
11.3%
M1148
 
10.1%
A1138
 
10.0%
I800
 
7.0%
T713
 
6.3%
C632
 
5.5%
D626
 
5.5%
O575
 
5.0%
W539
 
4.7%
V503
 
4.4%
Other values (14)3436
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)11400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N1290
 
11.3%
M1148
 
10.1%
A1138
 
10.0%
I800
 
7.0%
T713
 
6.3%
C632
 
5.5%
D626
 
5.5%
O575
 
5.0%
W539
 
4.7%
V503
 
4.4%
Other values (14)3436
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N1290
 
11.3%
M1148
 
10.1%
A1138
 
10.0%
I800
 
7.0%
T713
 
6.3%
C632
 
5.5%
D626
 
5.5%
O575
 
5.0%
W539
 
4.7%
V503
 
4.4%
Other values (14)3436
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N1290
 
11.3%
M1148
 
10.1%
A1138
 
10.0%
I800
 
7.0%
T713
 
6.3%
C632
 
5.5%
D626
 
5.5%
O575
 
5.0%
W539
 
4.7%
V503
 
4.4%
Other values (14)3436
30.1%

Area Code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size289.6 KiB
415
2767 
510
1479 
408
1454 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17100
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row415
2nd row415
3rd row415
4th row408
5th row415

Common Values

ValueCountFrequency (%)
4152767
48.5%
5101479
25.9%
4081454
25.5%

Length

2025-10-31T14:56:45.549235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T14:56:45.628154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4152767
48.5%
5101479
25.9%
4081454
25.5%

Most occurring characters

ValueCountFrequency (%)
14246
24.8%
54246
24.8%
44221
24.7%
02933
17.2%
81454
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)17100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14246
24.8%
54246
24.8%
44221
24.7%
02933
17.2%
81454
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14246
24.8%
54246
24.8%
44221
24.7%
02933
17.2%
81454
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14246
24.8%
54246
24.8%
44221
24.7%
02933
17.2%
81454
 
8.5%

Phone
Text

Distinct3333
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Memory size317.4 KiB
2025-10-31T14:56:45.937528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters45600
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2854 ?
Unique (%)50.1%

Sample

1st row382-4657
2nd row371-7191
3rd row358-1921
4th row375-9999
5th row330-6626
ValueCountFrequency (%)
330-263514
 
0.2%
360-117113
 
0.2%
328-783312
 
0.2%
394-840212
 
0.2%
353-337211
 
0.2%
348-719311
 
0.2%
341-989011
 
0.2%
385-899711
 
0.2%
387-111611
 
0.2%
369-362611
 
0.2%
Other values (3323)5583
97.9%
2025-10-31T14:56:46.361686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37800
17.1%
-5700
12.5%
44781
10.5%
73655
8.0%
93539
7.8%
63525
7.7%
83506
7.7%
53496
7.7%
13445
7.6%
23144
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)45600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
37800
17.1%
-5700
12.5%
44781
10.5%
73655
8.0%
93539
7.8%
63525
7.7%
83506
7.7%
53496
7.7%
13445
7.6%
23144
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
37800
17.1%
-5700
12.5%
44781
10.5%
73655
8.0%
93539
7.8%
63525
7.7%
83506
7.7%
53496
7.7%
13445
7.6%
23144
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
37800
17.1%
-5700
12.5%
44781
10.5%
73655
8.0%
93539
7.8%
63525
7.7%
83506
7.7%
53496
7.7%
13445
7.6%
23144
6.9%

Churn
Categorical

Uniform 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size278.4 KiB
0
2850 
1
2850 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02850
50.0%
12850
50.0%

Length

2025-10-31T14:56:46.496049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T14:56:46.565012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02850
50.0%
12850
50.0%

Most occurring characters

ValueCountFrequency (%)
02850
50.0%
12850
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02850
50.0%
12850
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02850
50.0%
12850
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02850
50.0%
12850
50.0%

Interactions

2025-10-31T14:56:36.684525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:07.363865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:09.206339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:11.222893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:13.633079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:16.016521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:18.628089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:20.772825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-31T14:56:36.442812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:38.440850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:09.090839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:11.104911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:13.469052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:15.904436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:18.514072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:20.647386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:22.372479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:24.441005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:26.584968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:28.851862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:30.650145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:32.838311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:34.667941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T14:56:36.562510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-31T14:56:46.658009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Account LengthArea CodeChurnCustServ CallsDay CallsDay ChargeDay MinsEve CallsEve ChargeEve MinsIntl CallsIntl ChargeIntl MinsIntl PlanNight CallsNight ChargeNight MinsVMail MessageVMail Plan
Account Length1.0000.0680.0330.0130.0200.0140.0140.034-0.010-0.0100.0260.0180.0180.0960.003-0.054-0.0540.0190.051
Area Code0.0681.0000.0180.0740.0650.0650.0650.0640.0780.0790.0790.0380.0380.0460.0510.0330.0350.0380.000
Churn0.0330.0181.0000.3300.0760.3930.3940.0480.1180.1180.1300.1010.1010.2900.0690.0710.0730.1820.151
CustServ Calls0.0130.0740.3301.000-0.017-0.174-0.174-0.002-0.081-0.081-0.022-0.038-0.0380.109-0.015-0.043-0.0430.0040.100
Day Calls0.0200.0650.076-0.0171.0000.0390.0390.0230.0170.0170.0070.0060.0060.100-0.0260.0090.009-0.0140.046
Day Charge0.0140.0650.393-0.1740.0391.0001.0000.0200.1510.1510.029-0.002-0.0020.1000.0490.0650.065-0.1060.161
Day Mins0.0140.0650.394-0.1740.0391.0001.0000.0200.1510.1510.029-0.002-0.0020.1000.0490.0650.065-0.1060.161
Eve Calls0.0340.0640.048-0.0020.0230.0200.0201.0000.0080.0080.033-0.018-0.0180.0810.012-0.005-0.0050.0090.039
Eve Charge-0.0100.0780.118-0.0810.0170.1510.1510.0081.0001.000-0.0070.0130.0130.0760.044-0.007-0.007-0.0290.036
Eve Mins-0.0100.0790.118-0.0810.0170.1510.1510.0081.0001.000-0.0070.0130.0130.0760.044-0.007-0.007-0.0290.035
Intl Calls0.0260.0790.130-0.0220.0070.0290.0290.033-0.007-0.0071.0000.0370.0370.1500.0120.0190.0190.0400.059
Intl Charge0.0180.0380.101-0.0380.006-0.002-0.002-0.0180.0130.0130.0371.0001.0000.1660.031-0.022-0.0220.0200.025
Intl Mins0.0180.0380.101-0.0380.006-0.002-0.002-0.0180.0130.0130.0371.0001.0000.1660.031-0.022-0.0220.0200.025
Intl Plan0.0960.0460.2900.1090.1000.1000.1000.0810.0760.0760.1500.1660.1661.0000.0780.0920.0910.1110.052
Night Calls0.0030.0510.069-0.015-0.0260.0490.0490.0120.0440.0440.0120.0310.0310.0781.0000.0050.0050.0190.056
Night Charge-0.0540.0330.071-0.0430.0090.0650.065-0.005-0.007-0.0070.019-0.022-0.0220.0920.0051.0001.000-0.0340.066
Night Mins-0.0540.0350.073-0.0430.0090.0650.065-0.005-0.007-0.0070.019-0.022-0.0220.0910.0051.0001.000-0.0340.068
VMail Message0.0190.0380.1820.004-0.014-0.106-0.1060.009-0.029-0.0290.0400.0200.0200.1110.019-0.034-0.0341.0000.999
VMail Plan0.0510.0000.1510.1000.0460.1610.1610.0390.0360.0350.0590.0250.0250.0520.0560.0660.0680.9991.000

Missing values

2025-10-31T14:56:38.708804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-31T14:56:39.579503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Account LengthVMail MessageDay MinsEve MinsNight MinsIntl MinsCustServ CallsIntl PlanVMail PlanDay CallsDay ChargeEve CallsEve ChargeNight CallsNight ChargeIntl CallsIntl ChargeStateArea CodePhoneChurn
012825265.1197.4244.710.010111045.079916.789111.0132.70KS415382-46570
110726161.6195.5254.413.710112327.4710316.6210311.4533.70OH415371-71910
21370243.4121.2162.612.200011441.3811010.301047.3253.29NJ415358-19210
3840299.461.9196.96.62107150.90885.26898.8671.78OH408375-99990
4750166.7148.3186.910.131011328.3412212.611218.4132.73OK415330-66260
51180223.4220.6203.96.30109837.9810118.751189.1861.70AL510391-80270
612124218.2348.5212.67.53018837.0910829.621189.5772.03MA510355-99930
71470157.0103.1211.87.10107926.69948.76969.5361.92MO415329-90010
81170184.5351.6215.88.71009731.378029.89909.7142.35LA408335-47190
914137258.6222.0326.411.20118443.9611118.879714.6953.02WV415330-81730
Account LengthVMail MessageDay MinsEve MinsNight MinsIntl MinsCustServ CallsIntl PlanVMail PlanDay CallsDay ChargeEve CallsEve ChargeNight CallsNight ChargeIntl CallsIntl ChargeStateArea CodePhoneChurn
56901490119.2168.3204.712.24008820.2611014.311199.2163.29NH415368-77061
569113434247.2225.5186.36.120110542.0213319.17768.3851.65NJ510373-39591
56921160133.3247.8219.011.35009422.6612621.06789.8653.05MI408379-25031
5693740282.5219.9170.09.410011448.034818.691157.6542.54NV415355-68371
56941690266.7158.2287.713.830010545.348813.4511112.9533.73TX408379-58851
56952240171.5160.0212.45.01109929.1610313.601029.5621.35DE510361-65631
56961310131.6179.3251.215.51009522.3710915.2412911.3034.19MS415333-90021
56971320291.2234.2191.78.910010449.5013219.91878.6332.40MI408389-46081
56981000113.3197.9284.511.74009619.268916.829312.8023.16MT415341-48731
56991470274.0231.8283.66.20009246.588219.708312.7611.67MD408376-42921

Duplicate rows

Most frequently occurring

Account LengthVMail MessageDay MinsEve MinsNight MinsIntl MinsCustServ CallsIntl PlanVMail PlanDay CallsDay ChargeEve CallsEve ChargeNight CallsNight ChargeIntl CallsIntl ChargeStateArea CodePhoneChurn# duplicates
58570115.0122.3245.06.40106519.559610.407511.0311.73NJ510330-2635114
407144061.677.1173.08.240011710.47856.55997.7972.21NY408360-1171113
16088073.3161.4239.68.24008612.468213.727610.7832.21SC510394-8402112
216980227.1120.5117.04.750011638.6110310.241025.2741.27AR415328-7833112
11240149.0131.0238.68.62007325.338111.146910.7432.32DC408369-3626111
43500131.1160.5206.95.650012922.299413.64889.3191.51WV408348-7193111
222990256.4214.5233.77.93004443.5910518.237510.5212.13OR408353-3372111
2301000278.0176.7219.58.30007647.267415.021269.8842.24OH510385-8997111
2611080115.1211.3136.113.821011419.577017.96856.1233.73MI408341-9890111
3411230242.2226.1268.68.25008741.1710119.2212112.0932.21WY415387-1116111